Bearing fault diagnosis based on variational autoencoder and non-local block wide kernel convolutional neural network

Abstract

At present, convolutional neural network (CNN) is widely applied to bearing fault diagnosis However, the diagnosis performance will descend under the strong noise condition in the real industrial environment. Therefore, a denoising method named non-local block wide kernel CNN (NLBWCNN) is proposed based on wide convolution kernel and non-local block. Additionally, the data in the mechanical fault state is less than that in the health state in actual industrial production, which leads to the data imbalance problem. However, the fault classifier based on CNN needs a large amount of balanced data to train. Otherwise, it will not be fully trained, and thus its generalization ability will be affected. As a result, a method called VAE-NLBWCNN (variational autoencoder and NLBWCNN) is proposed for diagnosing bearing faults. The method employs variational autoencoder balanced the fault data. And then, the NLBWCNN is utilized to denoise and classify the fault data. The proposed VAE-NLBWCNN method is validated on three bearing datasets. The comparative experiments demonstrate that the proposed method can effectively expand unbalanced data and achieve the best performance in various noise conditions.